Multivariable Learning Using Frequency Response Data: A Robust Iterative Inversion-Based Control Approach with Application
Multivariable Learning Using Frequency Response Data: A Robust Iterative Inversion-Based Control Approach with Application
复制标题
使用频率响应数据的多变量学习：一种基于迭代反演的鲁棒控制方法及其应用
DOI：
10.23919/acc.2019.8814971
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
T. Oomen
中科院分区：
文献类型：
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作者：
R. Rozario;Juliana Langen;T. Oomen
学习控制方法能够显著提高重复运行系统的性能。典型的方法依赖于一个参数化的植物模型，以实现快速和鲁棒的收敛。本文的目的是开发一个多变量系统的框架，使快速和鲁棒的学习，而不需要一个参数化的工厂模型。这是通过连接非参数频率响应函数识别和鲁棒控制，使合成的频率频率的基础上。一个非保守的方法，通过确保所确定的不确定性是直接与发达国家的综合框架兼容。多变量基准运动系统的应用程序证实了开发的框架的潜力。
Learning control methods enable significant performance improvements for systems that operate repetitively. Typical methods rely on a parametric plant model to achieve fast and robust convergence. The aim of this paper is to develop a framework for multivariable systems that enables fast and robust learning without requiring a parametric plant model. This is achieved by connecting nonparametric frequency response function identification and robust control, which enables synthesis on a frequency-by-frequency basis. A nonconservative approach is obtained by ensuring that the identified uncertainty is directly compatible with the developed synthesis framework. Application to a multivariable benchmark motion system confirms the potential of the developed framework.